{"title":"基于深度学习的 PLC 系统信道估计","authors":"Nasser Sadeghi, Masoumeh Azghani","doi":"10.1007/s12243-024-01051-3","DOIUrl":null,"url":null,"abstract":"<p>Power line communication systems (PLC) are used for data transmission. Accurate channel state information (CSI) is essential for the receiver design in such systems, however, impulsive noise poses a challenge for the channel estimation task. In this paper, we propose a deep learning based method for PLC channel estimation which is resistant against impulsive noise as well as the additive white Gaussian noise (AWGN). The proposed deep neural network consists of three sub-networks: The first one is a denoising network which aims to remove the noise from the received signal. The second sub-network offers a low-accuracy estimation of the channel using the denoised signal. The third sub-network is designed for high-accuracy channel estimation. The training of the proposed network is done in two stages: Firstly, the denoising sub-network is trained. Secondly, by freezing the trained parameters of the denoising network, the two-channel estimation sub-networks are trained. Moreover, we have derived the Cramer Rao lower bound of the PLC channel estimation problem. The proposed method has been evaluated through various simulation scenarios which confirm the superiority of the proposed method over its counterpart. The suggested algorithm indicates acceptable resistance against impulsive and Gaussian noises.</p>","PeriodicalId":50761,"journal":{"name":"Annals of Telecommunications","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep learning based channel estimation in PLC systems\",\"authors\":\"Nasser Sadeghi, Masoumeh Azghani\",\"doi\":\"10.1007/s12243-024-01051-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Power line communication systems (PLC) are used for data transmission. Accurate channel state information (CSI) is essential for the receiver design in such systems, however, impulsive noise poses a challenge for the channel estimation task. In this paper, we propose a deep learning based method for PLC channel estimation which is resistant against impulsive noise as well as the additive white Gaussian noise (AWGN). The proposed deep neural network consists of three sub-networks: The first one is a denoising network which aims to remove the noise from the received signal. The second sub-network offers a low-accuracy estimation of the channel using the denoised signal. The third sub-network is designed for high-accuracy channel estimation. The training of the proposed network is done in two stages: Firstly, the denoising sub-network is trained. Secondly, by freezing the trained parameters of the denoising network, the two-channel estimation sub-networks are trained. Moreover, we have derived the Cramer Rao lower bound of the PLC channel estimation problem. The proposed method has been evaluated through various simulation scenarios which confirm the superiority of the proposed method over its counterpart. The suggested algorithm indicates acceptable resistance against impulsive and Gaussian noises.</p>\",\"PeriodicalId\":50761,\"journal\":{\"name\":\"Annals of Telecommunications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-07-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Telecommunications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s12243-024-01051-3\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"TELECOMMUNICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Telecommunications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12243-024-01051-3","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
Deep learning based channel estimation in PLC systems
Power line communication systems (PLC) are used for data transmission. Accurate channel state information (CSI) is essential for the receiver design in such systems, however, impulsive noise poses a challenge for the channel estimation task. In this paper, we propose a deep learning based method for PLC channel estimation which is resistant against impulsive noise as well as the additive white Gaussian noise (AWGN). The proposed deep neural network consists of three sub-networks: The first one is a denoising network which aims to remove the noise from the received signal. The second sub-network offers a low-accuracy estimation of the channel using the denoised signal. The third sub-network is designed for high-accuracy channel estimation. The training of the proposed network is done in two stages: Firstly, the denoising sub-network is trained. Secondly, by freezing the trained parameters of the denoising network, the two-channel estimation sub-networks are trained. Moreover, we have derived the Cramer Rao lower bound of the PLC channel estimation problem. The proposed method has been evaluated through various simulation scenarios which confirm the superiority of the proposed method over its counterpart. The suggested algorithm indicates acceptable resistance against impulsive and Gaussian noises.
期刊介绍:
Annals of Telecommunications is an international journal publishing original peer-reviewed papers in the field of telecommunications. It covers all the essential branches of modern telecommunications, ranging from digital communications to communication networks and the internet, to software, protocols and services, uses and economics. This large spectrum of topics accounts for the rapid convergence through telecommunications of the underlying technologies in computers, communications, content management towards the emergence of the information and knowledge society. As a consequence, the Journal provides a medium for exchanging research results and technological achievements accomplished by the European and international scientific community from academia and industry.